Multi-View Clustering on Relational Data

Abstract : Clustering is a popular task in knowledge discovery. In this chapter we illustrate this fact with a new clustering algorithm that is able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. The advantages of this algorithm are threefold: it uses any dissimilarities between objects, it automatically ponderates the impact of each dissimilarity matrice and it provides interpretation tools. We illustrate the usefulness of this clustering method with two experiments. The first one uses a data set concerning handwritten numbers (digitized pictures) that must be recognized. The second uses a set of reports for which we have an expert classification given a priori so we can compare this classification with the one obtained automatically.
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Chapitre d'ouvrage
Fabrice Guillet and Bruno Pinaud and Gilles Venturini and Djamel Abdelkader Zighed. Advances in Knowledge Discovery and management, vol. 4, 527, Springer International Publishing, pp.37-51, 2013, Studies in Computational Intelligence, 978-3-319-02998-6 (Print) 978-3-319-02999-3 (Online). 〈10.1007/978-3-319-02999-3〉
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https://hal.inria.fr/hal-00904524
Contributeur : Thierry Despeyroux <>
Soumis le : jeudi 14 novembre 2013 - 16:04:35
Dernière modification le : mardi 17 avril 2018 - 11:24:42

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Thierry Despeyroux, Francisco de A.T. De Carvahlo, Yves Lechevallier, Filipe De Melo. Multi-View Clustering on Relational Data. Fabrice Guillet and Bruno Pinaud and Gilles Venturini and Djamel Abdelkader Zighed. Advances in Knowledge Discovery and management, vol. 4, 527, Springer International Publishing, pp.37-51, 2013, Studies in Computational Intelligence, 978-3-319-02998-6 (Print) 978-3-319-02999-3 (Online). 〈10.1007/978-3-319-02999-3〉. 〈hal-00904524〉

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